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Found 80 papers, 5 papers with code
WME 3.0: An Enhanced and Validated Lexicon of Medical Concepts
Information extraction in the medical domain is laborious and time-consuming due to the insufficient number of domain-specific lexicons and lack of involvement of domain experts such as doctors and medical practitioners.
Classification of COVID19 tweets using Machine Learning Approaches
The steps for pre-processing tweets, feature extraction, and the development of the machine learning models, are described extensively in the documentation.
Sentiment Classification of Code-Mixed Tweets using Bi-Directional RNN and Language Tags
In this work, we take up a similar challenge of developing a sentiment analysis model that can work with English-Tamil code-mixed data.
JUNLP@ICON2020: Low Resourced Machine Translation for Indic Languages
In the current work, we present the description of the systems submitted to a machine translation shared task organized by ICON 2020: 17th International Conference on Natural Language Processing.
WME: Sense, Polarity and Affinity based Concept Resource for Medical Events
In order to overcome the lack of medical corpora, we have developed a WordNet for Medical Events (WME) for identifying medical terms and their sense related information using a seed list.
Classifying Emotional Utterances by Employing Multi-modal Speech Emotion Recognition
In addition, a database consists of emotional utterances of several words has also been developed as a part of this work.
Studies Towards Language Independent Fake News Detection
We build two types of model one is language dependent and other one is language independent.
Leveraging Expectation Maximization for Identifying Claims in Low Resource Indian Languages
Furthermore, we used different ratios of manually labeled data and weakly labeled data to train our various machine learning models.
A Model of Competitive Assortment Planning Algorithm
The current paper suggests a model and discusses how competition and collusion arise in the digital marketplace through assortment planning or assortment optimization algorithm.
Complementarity in Demand-side Variables and Educational Participation
Decision to participate in education depends on the circumstances individual inherits and on the returns to education she expects as well.
Measurement of Trustworthiness of the Online Reviews
In this article, the researcher aims at formally deriving a rationality pattern function and thereby, the degree of rationality of the decision-maker or the reviewer in the sequential choice problem in the e-commerce markets.
JU_NLP at HinglishEval: Quality Evaluation of the Low-Resource Code-Mixed Hinglish Text
In this paper we describe a system submitted to the INLG 2022 Generation Challenge (GenChal) on Quality Evaluation of the Low-Resource Synthetically Generated Code-Mixed Hinglish Text.
Can Unsupervised Knowledge Transfer from Social Discussions Help Argument Mining?
Identifying argument components from unstructured texts and predicting the relationships expressed among them are two primary steps of argument mining.
AdvCodeMix: Adversarial Attack on Code-Mixed Data
Research on adversarial attacks are becoming widely popular in the recent years.
Incomplete Gamma Integrals for Deep Cascade Prediction using Content, Network, and Exogenous Signals
Second, there is a measurable positive correlation between the novelty of the root content (with respect to a streaming external corpus) and the relative size of the resulting cascade.
JUNLP at SemEval-2020 Task 9: Sentiment Analysis of Hindi-English Code Mixed Data Using Grid Search Cross Validation
This linguistic phenomenon poses a great challenge to conventional NLP domains such as Sentiment Analysis, Machine Translation, and Text Summarization, to name a few.
JUNLP@Dravidian-CodeMix-FIRE2020: Sentiment Classification of Code-Mixed Tweets using Bi-Directional RNN and Language Tags
Sentiment analysis has been an active area of research in the past two decades and recently, with the advent of social media, there has been an increasing demand for sentiment analysis on social media texts.
Development of POS tagger for English-Bengali Code-Mixed data
The proposed system is a modular approach that starts by tagging individual tokens with their respective languages and then passes them to different POS taggers, designed for different languages (English and Bengali, in our case).
Preparation of Sentiment tagged Parallel Corpus and Testing its effect on Machine Translation
The preparation of raw parallel corpus, sentiment analysis of the sentences and the training of a Character Based Neural Machine Translation model using the same has been discussed extensively in this paper.
JUNLP@SemEval-2020 Task 9:Sentiment Analysis of Hindi-English code mixed data using Grid Search Cross Validation
This linguistic phenomenon poses a great challenge to conventional NLP domains such as Sentiment Analysis, Machine Translation, and Text Summarization, to name a few.
Investigating Deep Learning Approaches for Hate Speech Detection in Social Media
Moreover, the varieties in user-generated data and the presence of various forms of hate speech makes it very challenging to identify the degree and intention of the message.
Code-Mixed to Monolingual Translation Framework
The use of multilingualism in the new generation is widespread in the form of code-mixed data on social media, and therefore a robust translation system is required for catering to the monolingual users, as well as for easier comprehension by language processing models.
K-TanH: Efficient TanH For Deep Learning
We propose K-TanH, a novel, highly accurate, hardware efficient approximation of popular activation function TanH for Deep Learning.
High Performance Scalable FPGA Accelerator for Deep Neural Networks
Low-precision is the first order knob for achieving higher Artificial Intelligence Operations (AI-TOPS).
Modeling Engagement Dynamics of Online Discussions using Relativistic Gravitational Theory
Unlike previous studies which model a discussion in a static manner, in the present study, we model it as a time-varying process and solve two inter-related problems -- predict which user groups will get engaged with an ongoing discussion, and forecast the growth rate of a discussion in terms of the number of comments.
JUCBNMT at WMT2018 News Translation Task: Character Based Neural Machine Translation of Finnish to English
In the current work, we present a description of the system submitted to WMT 2018 News Translation Shared task.
JUMT at WMT2019 News Translation Task: A Hybrid approach to Machine Translation for Lithuanian to English
In the current work, we present a description of the system submitted to WMT 2019 News Translation Shared task.
NLP at SemEval-2019 Task 6: Detecting Offensive language using Neural Networks
The three best models that performed best on individual sub tasks are stacking of CNN-Bi-LSTM with Attention, BiLSTM with POS information added with word features and Bi-LSTM for third task.
A Study of BFLOAT16 for Deep Learning Training
In this paper, we discuss the flow of tensors and various key operations in mixed precision training, and delve into details of operations, such as the rounding modes for converting FP32 tensors to BFLOAT16.
Mixed Precision Training With 8-bit Floating Point
Reduced precision computation for deep neural networks is one of the key areas addressing the widening compute gap driven by an exponential growth in model size.
SMT vs NMT: A Comparison over Hindi & Bengali Simple Sentences
All of the systems use English-Hindi and English-Bengali language pairs containing simple sentences as well as sentences of other complexity.
Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifiers
In conjunction with the standard cross-entropy loss, we minimize the novel loss to train an ensemble of classifiers.
How did the discussion go: Discourse act classification in social media conversations
Our proposed model outperformed the previous one in terms of domain independence; without using platform-dependent structural features, our hierarchical LSTM with word relevance attention mechanism achieved F1-scores of 71\% and 66\% respectively to predict discourse roles of comments in Reddit and Facebook discussions.
Sentiment Analysis of Code-Mixed Indian Languages: An Overview of SAIL_Code-Mixed Shared Task @ICON-2017
This paper presents overview of the shared task on sentiment analysis of code-mixed data pairs of Hindi-English and Bengali-English collected from the different social media platform.
Preparing Bengali-English Code-Mixed Corpus for Sentiment Analysis of Indian Languages
To gather attention and encourage researchers to work on this crisis, we prepared gold standard Bengali-English code-mixed data with language and polarity tag for sentiment analysis purposes.
Language Identification of Bengali-English Code-Mixed data using Character & Phonetic based LSTM Models
Language identification of social media text still remains a challenging task due to properties like code-mixing and inconsistent phonetic transliterations.
Mixed Precision Training of Convolutional Neural Networks using Integer Operations
The state-of-the-art (SOTA) for mixed precision training is dominated by variants of low precision floating point operations, and in particular, FP16 accumulating into FP32 Micikevicius et al. (2017).
On Scale-out Deep Learning Training for Cloud and HPC
The exponential growth in use of large deep neural networks has accelerated the need for training these deep neural networks in hours or even minutes.
Analyzing Roles of Classifiers and Code-Mixed factors for Sentiment Identification
We have also tested various models trained on code-mixed data, as well as English features and the highest accuracy of 72. 50% was obtained by a Support Vector Machine (SVM) model.
NITMZ-JU at IJCNLP-2017 Task 4: Customer Feedback Analysis
In this paper, we describe a deep learning framework for analyzing the customer feedback as part of our participation in the shared task on Customer Feedback Analysis at the 8th International Joint Conference on Natural Language Processing (IJCNLP 2017).
JU NITM at IJCNLP-2017 Task 5: A Classification Approach for Answer Selection in Multi-choice Question Answering System
The main aim of this shared task is to choose the correct option for each multi-choice question.
JUNLP at IJCNLP-2017 Task 3: A Rank Prediction Model for Review Opinion Diversification
IJCNLP-17 Review Opinion Diversification (RevOpiD-2017) task has been designed for ranking the top-k reviews of a product from a set of reviews, which assists in identifying a summarized output to express the opinion of the entire review set.
Identification of Character Adjectives from Mahabharata
along with deep learning to classify the patterns as characters or non-characters in order to achieve decent accuracy.
BUCC2017: A Hybrid Approach for Identifying Parallel Sentences in Comparable Corpora
A Statistical Machine Translation (SMT) system is always trained using large parallel corpus to produce effective translation.
RAIL: Risk-Averse Imitation Learning
Generative Adversarial Imitation Learning (GAIL) is a state-of-the-art algorithm for learning policies when the expert's behavior is available as a fixed set of trajectories.
Ternary Residual Networks
Aided by such an elegant trade-off between accuracy and compute, the 8-2 model (8-bit activations, ternary weights), enhanced by ternary residual edges, turns out to be sophisticated enough to achieve very high accuracy ($\sim 1\%$ drop from our FP-32 baseline), despite $\sim 1. 6\times$ reduction in model size, $\sim 26\times$ reduction in number of multiplications, and potentially $\sim 2\times$ power-performance gain comparing to 8-8 representation, on the state-of-the-art deep network ResNet-101 pre-trained on ImageNet dataset.
Determining sentiment in citation text and analyzing its impact on the proposed ranking index
Analysis of these sentiments is one of the popular research areas of present day researchers.
Complexity Metric for Code-Mixed Social Media Text
Also, the index can be applied to a sentence and seamlessly extended to a paragraph or an entire document.
Sentiment Identification in Code-Mixed Social Media Text
While some tasks deal with identifying the presence of sentiment in the text (Subjectivity analysis), other tasks aim at determining the polarity of the text categorizing them as positive, negative and neutral.
Ternary Neural Networks with Fine-Grained Quantization
We address this by fine-tuning Resnet-50 with 8-bit activations and ternary weights at $N=64$, improving the Top-1 accuracy to within $4\%$ of the full precision result with $<30\%$ additional training overhead.
Mixed Low-precision Deep Learning Inference using Dynamic Fixed Point
We propose a cluster-based quantization method to convert pre-trained full precision weights into ternary weights with minimal impact on the accuracy.
Multimodal Mood Classification - A Case Study of Differences in Hindi and Western Songs
Finally, we developed mood classification systems using Support Vector Machines and Feed Forward Neural Networks based on the features collected from audio, lyrics, and a combination of both.
Labeling of Query Words using Conditional Random Field
Our system demonstrated an overall accuracy of 75. 5% for token level language identification.
Authorship Verification - An Approach based on Random Forest
We have used several word-based and style-based features to identify the dif-ferences between the known and unknown problems of one given set and label the unknown ones accordingly using a Random Forest based classifier.
Distributed Deep Learning Using Synchronous Stochastic Gradient Descent
We design and implement a distributed multinode synchronous SGD algorithm, without altering hyper parameters, or compressing data, or altering algorithmic behavior.
Identifying Bengali Multiword Expressions using Semantic Clustering
As a by-product of this experiment, we have started developing a standard lexicon in Bengali that serves as a productive Bengali linguistic thesaurus.
